Recognize text from picture

sodoku

So good news I got an iPhone 11 and I’m testing this on it, for sudoku, do the pictures taken save anywhere when used, just curious if I have to delete them because after I use it the pictures don’t show up in my pictures app

sodoku

Also what’s the updated code posted by Mikael on GitHub used for is it same as this one posted here or not because it’s so much longer and bigger then this code posted on the forum, is it a better version then this one on the forum

mikael

@sodoku, the code on Github is more of a tool, and much faster than the version at the beginning of this thread. For your purposes, you probably just want pieces of it.

It supports taking a picture normally and then selecting it from the photo library when you use the tool, or just snapping a quick ”disposable” in-tool image which is not saved.

pavlinb

This post is deleted!

sodoku

So there are a few edits in this thread I don’t know how to piece together to have the best edited version of this???

mikael

@sodoku, the one on github is the latest version.

sodoku

I will test it for sudoku in the console, well I will try to convert it to use in console for the sudoku solver if need help I’ll post a message

sodoku

I need help adapting this script for inputting the numbers from a picture of sudoku and insert the starting numbers into a console script, it does not work good for recognizing ones and sevens???

Example of sudoku solver

In my version I want to make the board is all zeros and when you take a picture it will add the numbers than solve, this combines the two programs (sudoku solver) & (ocr text recognition)

board=[
   [5,8,4,1,0,0,0,0,0],
   [0,0,6,8,0,0,5,1,0],
   [0,0,0,0,5,4,7,0,6],
   [0,5,3,0,1,0,0,6,7],
   [0,0,0,0,2,0,0,0,0],
   [4,6,0,0,9,0,8,3,0],
   [7,0,8,5,4,0,0,0,0],
   [0,2,9,0,0,3,4,0,0],
   [0,0,0,0,0,1,3,7,9]
   ]



def solve(bo):

   find = find_empty(bo)
   if not find:
   	return True
   else:
   	row,col = find

   for i in range(1,10):
   	if valid(bo,i,(row,col)):
   		bo[row][col] = i

   		if solve(bo):
   			return True

   		bo[row][col] = 0

   return False



def valid(bo,num,pos):
   #check row
   for i in range(len(bo[0])):
   	if bo[pos[0]][i] == num and pos[1] != i:
   		return False
   #check column
   for i in range(len(bo[0])):
   	if bo[i][pos[1]] == num and pos[0] != i:
   		return False
   #check quadrant
   box_x = pos[1] // 3
   box_y = pos[0] // 3

   for i in range(box_y * 3, box_y * 3 + 3):
   	for j in range(box_x * 3, box_x * 3 + 3):
   		if bo[i][j] == num and (i,j) != pos:
   			return False

   return True



def print_board(bo):
   for i in range(len(bo)):
   	if i % 3 == 0 and i != 0:
   		print('------+-------+------')

   	for j in range(len(bo[0])):
   		if j % 3 == 0 and j != 0:
   			print('|',end=' ')
   		if j == 8:
   			print(bo[i][j])
   		else:
   			print(str(bo[i][j])+ ' ', end='')

def find_empty(bo):
   for i in range(len(bo)):
   	for j in range(len(bo[0])):
   		if bo[i][j] == 0:
   			return (i,j) # row, col
   return None



print_board(board)
solve(board)
print('=====================')
print_board(board)

language_preference = ['fi','en','se']

import photos, ui, dialogs
import io
from objc_util import *

load_framework('Vision')
VNRecognizeTextRequest = ObjCClass('VNRecognizeTextRequest')
VNImageRequestHandler = ObjCClass('VNImageRequestHandler')

def pil2ui(pil_image):
   buffer = io.BytesIO()
   pil_image.save(buffer, format='PNG')
   return ui.Image.from_data(buffer.getvalue())

selection = dialogs.alert('Get pic', button1='Camera', button2='Photos')

ui_image = None

if selection == 1:
   pil_image = photos.capture_image()
   if pil_image is not None:
       ui_image = pil2ui(pil_image)
elif selection == 2:
   ui_image = photos.pick_asset().get_ui_image()

if ui_image is not None:
   print('Recognizing...\n')

   req = VNRecognizeTextRequest.alloc().init().autorelease()
   req.setRecognitionLanguages_(language_preference)
   handler = VNImageRequestHandler.alloc().initWithData_options_(ui_image.to_png(), None).autorelease()

   success = handler.performRequests_error_([req], None)
   if success:
       for result in req.results():
           print(result.text())
   else:
       print('Problem recognizing anything') ```

mikael

@sodoku, I tried something similar as well a while ago, first recognizing rectangles and then trying to recognize the numbers, but I hit the same issue of very poor recognition of the numbers. I wonder if we would need a number-specific recognizer for that.

Spitfire

Hi, since the sudoko is a square of many squares I think it is more robust to slice the cells evenly and only have one nr in a small image.

Of course downside is to use a recognition service per image and you go from 1 image to 81 - that can get expensive.

But it would work more robust.
Best reg Tommy

mikael

@Spitfire, thanks. I did try all kinds of approaches, finally resorting to manual cropping, and it still was not reliable enough.

pavlinb

@mikael Could you share some picture of sudoku, where recognition fails?

ccc

Multiple sample Sudoku puzzles would help to achieve a robust solution.

sodoku

I have a few questions about the very first text recognition code posted on this one

the example video I am referring to is https://developer.apple.com/videos/play/wwdc2019/234

1 how do you change the recognition level from fast to accurate
example code from apple website I am not sure if its written in swift or objective c but it is like this :

myTextRegcognitionRequest.recognitionLevel = VNRequestTextRecognitionLevel.accurate

and another example of this shown in the apple video for setting the recognition level

 request.recognitionLevel = .fast

question 2
to ensure that numbers don't get mistaken as letters
without the language corrector active to avoid mistaking the number 5 for an S or I as 1
example of this from the video is

extension Character {
       
     func GetSimilarCharacterIfNotIn(allowedChars: String -> Character {
            let  conversionTable = [
                      's':'5',
                      'S':'5',
                      'i':'1',
                      'I':'1', ]

question 3
if you know how to set up the special words detector thingy feature mentioned in the video

westjensontexas

I gather they are looking for words you'd find in an English dictionary. So perhaps façade, or tête-à-tête might recognize, while other examples wouldn't? mobdro apk tubemate

JonB

@sodoku
See https://developer.apple.com/documentation/vision/vnrequesttextrecognitionlevel/fast

Try req.recognitionLevel=1 for fast, or 0 for accurate.

Re fixing characters... I gather you might set req.usesLanguageCorrection=False (or maybe 0), then make your own replacement map and use str.translate.

Custom words is handled by
req.customWords = ['customword1', 'etc']

See apple docs for VNRecognizeTextRequest

sodoku

ive seen the apple documentation coding on Vision Framework I just dont know how to convert it to python

Question 1
What about the setting the minimum text height how do you translate either of these codes to python????
@property(readwrite, nonatomic, assign) float minimumTextHeight; written in objective-c
var minimumTextHeight: Float { get set } written in Swift

Question 2
I was also interested in learning how to recognize the individual boxes from a sudoku puzzle to extract the numbers is there a way to do that possibly with
VNRecognizedTextObservation A request that detects and recognizes regions of text in an image.
or possibly with the bounding box technique show in the video https://developer.apple.com/videos/play/wwdc2019/234 , also can you put multiple bounding boxes to recognize text from a sudoku card

this is Mikeals code i am trying to insert the code into but dont know how to convert the code shown in the apple documentation into python

language_preference = ['fi','en','se']

import photos, ui, dialogs
import io
from objc_util import *

load_framework('Vision')
VNRecognizeTextRequest = ObjCClass('VNRecognizeTextRequest')
VNImageRequestHandler = ObjCClass('VNImageRequestHandler')

def pil2ui(pil_image):
    buffer = io.BytesIO()
    pil_image.save(buffer, format='PNG')
    return ui.Image.from_data(buffer.getvalue())

selection = dialogs.alert('Get pic', button1='Camera', button2='Photos')

ui_image = None

if selection == 1:
    pil_image = photos.capture_image()
    if pil_image is not None:
        ui_image = pil2ui(pil_image)
elif selection == 2:
    ui_image = photos.pick_asset().get_ui_image()

if ui_image is not None:
    print('Recognizing...\n')

    req = VNRecognizeTextRequest.alloc().init().autorelease()
    req.recognitionLevel=1
    req.setRecognitionLanguages_(language_preference)
    handler = VNImageRequestHandler.alloc().initWithData_options_(ui_image.to_png(), None).autorelease()

    success = handler.performRequests_error_([req], None)
    if success:
        for result in req.results():
            print(result.text())
    else:
        print('Problem recognizing anything')

JonB

@sodoku For things like enumerations, you can usually check the swift version of docs, which tells you the value. Otherwise, you can often look up source code.

For minimumTextHeight, both swift and ObjC say this is a float. The fact that it is readwrite/nonatomic/assign is not important.
So, usually this would just be
req.minimumTextHeight = 32.5
or whatever you want...

It is often helpful to explore objects in the console, since this can tell you what you're working with. For instance, if you type req. in the console, you will see autocomplete of all known attributes. Usually you need to treat objc properties as function calls -- so to check minimumTextHeight, you'd use req.minimumTextHeight(). But to set, you can treat the property as a python attribute and assign directly. In some cases, you may need to use the set_propertyName_(value) convention.

Where things get tricky is where the declared type is another object (in which case you have to provide the right type of object), or a structure. Structures can be tricky because objc_util often screws up the type encodings, and you have to manually override. Structures get turned into python STRUCTUREs, and you access fields normally like you would with a python object (no () needed).

Re question 2:
Per the docs, the results of a request will be VNRecognizedTextObservation objects. This is a subclass of VNRectangleObservation.

@interface VNRecognizedTextObservation : VNRectangleObservation <-- colon here means inherits from

If you look up VNRectangleObservation, you will see it has the following attributes
bottomLeft
bottomRight
topLeft
topRight
Which are declared as CGPoint, which is a structure that has an .x and .y fields.

for result in req.results():
    x = result.bottomLeft().x
    y = result.bottomLeft().y
    w = result.topRight().x-x
    h = result.topRight().y-y
    print('({},{},{},{}) {}'.format(x,y,w,h, result.text())

You could draw the image into an image context, and then also stroke a rectangle.. something like this...(not tried).

with ui.ImageContext(ui_image.size()) as ctx:
   ui_image.draw()
   for result in req.results():
      vertecies = [(p.x, p.y) 
                               for p in [result.bottomLeft()
                                        result.TopLeft()
                                        result.TopRight()
                                        result.BottomRight()
                                        result.bottomLeft()]
      pth = ui.Path.moveTo(*vertecies[0]) %initial point
      for p in vertecies[1:]:
         pth.line_to(*p)  
      ui.set_color('red')
      pth.stroke()
      x,y = vertecies[0]
      w,h =(vertecies[2].x-x), (vertecies[2].y-y)
      ui.draw_string(result.text(), rect=(x,y,w,h), font=('<system>', 12), color='red')
   marked_img = ctx.get_image()
   marked_img.show()

JonB

I realized that result will also have a .boundingBox() attribute which would make some of this a little simpler.
That is a CGrect, consisting of .origin (in turn consisting of .x and .y and .size containing .w and .h.
In that case you could use ui.Path.rect.

JonB

Okay, my previous reply was full of errors... here is a working version, which adds red boxes around each result, along with the text

language_preference = ['fi','en','se']

import photos, ui, dialogs
import io
from objc_util import *

load_framework('Vision')
VNRecognizeTextRequest = ObjCClass('VNRecognizeTextRequest')
VNImageRequestHandler = ObjCClass('VNImageRequestHandler')

ACCURATE=0
FAST=1

def pil2ui(pil_image):
    buffer = io.BytesIO()
    pil_image.save(buffer, format='PNG')
    return ui.Image.from_data(buffer.getvalue())

selection = dialogs.alert('Get pic', button1='Camera', button2='Photos')

ui_image = None

if selection == 1:
    pil_image = photos.capture_image()
    if pil_image is not None:
        ui_image = pil2ui(pil_image)
elif selection == 2:
    ui_image = photos.pick_asset().get_ui_image()

if ui_image is not None:
    print('Recognizing...\n')

    req = VNRecognizeTextRequest.alloc().init().autorelease()
    req.recognitionLevel= ACCURATE# accurate
    req.setRecognitionLanguages_(language_preference)
    handler = VNImageRequestHandler.alloc().initWithData_options_(ui_image.to_png(), None).autorelease()

    success = handler.performRequests_error_([req], None)
    if success:
        for result in req.results():
            print(result.text())
    else:
        print('Problem recognizing anything')

with ui.ImageContext(*tuple(ui_image.size) ) as ctx:
   ui_image.draw()
   for result in req.results():
      cgpts=[   result.bottomLeft(),
                result.topLeft(),
                result.topRight(),
                result.bottomRight(),
                result.bottomLeft()  ] 
      vertecies = [(p.x*ui_image.size.w, (1-p.y)*ui_image.size.h) for p in cgpts]
      pth = ui.Path()
      pth.move_to(*vertecies[0]) 
      for p in vertecies[1:]:
         pth.line_to(*p)  
      ui.set_color('red')
      pth.stroke()
      x,y = vertecies[0]
      w,h =(vertecies[2][0]-x), (vertecies[2][1]-y)
      ui.draw_string(str(result.text()), rect=(x,y,w,h), font=('<system>', 12), color='red')
   marked_img = ctx.get_image()
   marked_img.show()